The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components.
These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.

The analysis and understanding of human movement is central to many applications
such as sports science, medical diagnosis and movie production. The ability to
automatically monitor human activity in security sensitive areas such as airports,
lobbies or borders is of great practical importance. Furthermore, automatic
pose estimation from images leverages the processing
and understanding of massive digital libraries available on the Internet.
We build upon a model based approach where the human shape is modelled with a surface mesh
and the motion is parametrized by a kinematic chain. We then seek for the pose
of the model that best explains the available observations coming from different sensors.
In a first scenario, we consider a calibrated mult-iview setup in an indoor studio. To obtain very accurate
results, we propose a novel tracker that combines information coming from video and a
small set of Inertial Measurement Units (IMUs). We do so by locally optimizing a joint
energy consisting of a term that measures the likelihood of the video data and a term
for the IMU data. This is the first work to successfully combine video and IMUs
information for full body pose estimation. When compared to commercial marker based systems
the proposed solution is more cost efficient and less intrusive for the user.
In a second scenario, we relax the assumption of an indoor studio and we tackle outdoor scenes
with background clutter, illumination changes, large recording volumes and difficult motions
of people interacting with objects. Again, we combine information from video and IMUs.
Here we employ a particle based optimization approach
that allows us to be more robust to tracking failures. To satisfy the orientation constraints
imposed by the IMUs, we derive an analytic Inverse Kinematics (IK) procedure to sample from the manifold
of valid poses. The generated hypothesis come from a lower dimensional manifold and therefore the computational
cost can be reduced. Experiments on challenging sequences suggest the proposed tracker can be applied
to capture in outdoor scenarios. Furthermore, the proposed IK sampling procedure can be used
to integrate any kind of constraints derived from the environment.
Finally, we consider the most challenging possible scenario: pose estimation of monocular images.
Here, we argue that estimating the pose to the degree of accuracy as in an engineered environment is
too ambitious with the current technology. Therefore, we propose to extract meaningful semantic information about
the pose directly from image features in a discriminative fashion. In particular, we introduce posebits
which are semantic pose descriptors about the geometric relationships between parts in the body.
The experiments
show that the intermediate step of inferring posebits from images can improve pose estimation from
monocular imagery. Furthermore, posebits can be very useful as input feature for many computer vision
algorithms.

2008

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems